class_docstr: Implement Alpha Suffix model averaging.
  This method is based on paper "Making Gradient Descent Optimalfor Strongly
  Convex Stochastic Optimization" (https://arxiv.org/pdf/1109.5647.pdf).
docstr: Taking iteration average for the last epoch*alpha epochs
  
  Parameters
  ----------
  epochs
      The total number of epochs.
  alpha
      Proportion of averaging.
  eta
      Parameter of polynomial-decay averaging.
docstr_tok: ["Taking","iteration","average","for","the","last","epoch","*","alpha","epochs","Parameters","--","--","--","--","--","epochs","The","total","number","of","epochs",".","alpha","Proportion","of","averaging",".","eta","Parameter","of","polynomial-decay","averaging","."]
code: def __init__(self, epochs: int, alpha: float = 0.75, eta: float = 0):
      r"""
      Taking iteration average for the last epoch*alpha epochs
  
      Parameters
      ----------
      epochs
          The total number of epochs.
      alpha
          Proportion of averaging.
      eta
          Parameter of polynomial-decay averaging.
      """
  
      super().__init__(eta=eta)
  
      assert 0 <= alpha <= 1
  
      # The epoch where iteration averaging starts.
      self.alpha_suffix = epochs * (1.0 - alpha)
  
imports: from typing import Any, Dict, List, Optional
  import logging
  import mxnet
  import mxnet.gluon.nn
  from mxnet import gluon
  from gluonts.core.component import validated
  from .callback import Callback
label_desc: Use image node_id 1 to create a new Alpha_Suffix object from inherited base classes: IterationAveragingStrategy with example: obj = Alpha_Suffix(epochs, alpha, eta)